Insights from multi-omics integration in complex disease primary tissues
Authors: Peter Kreitmaier, Georgia Katsoula, Eleftheria Zeggini
Journal: Trends in Genetics (2022)

Genome-wide association studies (GWAS) have revealed the genetic basis of complex diseases. Integrative studies investigating multi-omics data of disease-relevant primary tissues are needed to refine these insights.By highlighting recent integrative multi-omics studies in relevant tissues of four distinct complex diseases (type 2 diabetes, osteoarthritis, Alzheimer's disease, and systemic lupus erythematosus), we outline the usefulness of this approach across complex disease types.Multi-omics approaches have extended our biological understanding (e.g., functional interpretation of GWAS signals, construction of new molecular maps) and revealed potential clinically relevant insights (e.g., patient stratification, biomarker identification). Genome-wide association studies (GWAS) have provided insights into the genetic basis of complex diseases. In the next step, integrative multi-omics approaches can characterize molecular profiles in relevant primary tissues to reveal the mechanisms that underlie disease development. Here, we highlight recent progress in four examples of complex diseases generated by integrative studies: type 2 diabetes (T2D), osteoarthritis, Alzheimer's disease (AD), and systemic lupus erythematosus (SLE). High-resolution methodologies such as single-cell and spatial omics techniques will become even more important in the future. Furthermore, we emphasize the urgent need to include as yet understudied cell types and increase the diversity of studied populations. Genome-wide association studies (GWAS) have provided insights into the genetic basis of complex diseases. In the next step, integrative multi-omics approaches can characterize molecular profiles in relevant primary tissues to reveal the mechanisms that underlie disease development. Here, we highlight recent progress in four examples of complex diseases generated by integrative studies: type 2 diabetes (T2D), osteoarthritis, Alzheimer's disease (AD), and systemic lupus erythematosus (SLE). High-resolution methodologies such as single-cell and spatial omics techniques will become even more important in the future. Furthermore, we emphasize the urgent need to include as yet understudied cell types and increase the diversity of studied populations. Complex diseases are driven by a combination of multiple environmental and genetic factors. Due to their high prevalence (e.g., osteoarthritis: 40% over the age of 70 years [1.Cui A. et al.Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies.EClinicalMedicine. 2020; 29–30100587Abstract Full Text Full Text PDF PubMed Scopus (327) Google Scholar]; diabetes: 6.28% of the world population [2.Khan M.A.B. et al.Epidemiology of type 2 diabetes - global burden of disease and forecasted trends.J. Epidemiol. Glob. Health. 2020; 10: 107-111Crossref PubMed Google Scholar]), complex diseases represent a substantial burden for public health systems [3.Vos T. et al.Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019.Lancet. 2020; 396: 1204-1222Abstract Full Text Full Text PDF PubMed Scopus (4846) Google Scholar]. In the context of an aging population, this burden is predicted to increase in the future, underlining the importance of developing effective and personalized treatment methods, including discovery of novel drug targets (especially for drugs that have been approved in another context, referred to as drug repurposing), the identification of biomarkers, and improved patient stratification [4.Zeggini E. et al.Translational genomics and precision medicine: moving from the lab to the clinic.Science. 2019; 365: 1409-1413Crossref PubMed Scopus (86) Google Scholar]. GWAS have identified genetic risk loci implicated in complex diseases and have provided much-needed insights into their complex genetic architecture [5.Buniello A. et al.The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019.Nucleic Acids Res. 2019; 47: D1005-D1012Crossref PubMed Scopus (1935) Google Scholar]. However, translating genetic findings into clinical applications remains challenging across complex diseases. Issues include the strong linkage disequilibrium between variants on risk haplotypes (the actual causal variant of a risk locus often remains elusive) or the identification of effector genes of risk variants, particularly for variants in noncoding regions (see Glossary). Multi-omics data of human primary tissues provide molecular profiles of disease-relevant cell types, thus revealing insights beyond those derived from genetic studies. This molecular information will contribute to overcoming current challenges in translational efforts of complex diseases (Figure 1, Key figure). Briefly, omics data can be integrated with GWAS results to identify target genes of risk variants using causal inference (e.g., Mendelian randomization [6.Hemani G. et al.The MR-Base platform supports systematic causal inference across the human phenome.eLife. 2018; 7e34408Crossref PubMed Scopus (1872) Google Scholar] or colocalization approaches [7.Giambartolomei C. et al.Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.PLoS Genet. 2014; 10e1004383Crossref PubMed Scopus (1036) Google Scholar,8.Giambartolomei C. et al.A Bayesian framework for multiple trait colocalization from summary association statistics.Bioinformatics. 2018; 34: 2538-2545Crossref PubMed Scopus (113) Google Scholar]). Furthermore, omics data can improve risk variant characterization, especially for those residing in noncoding sequence. Indeed, computational intersections of GWAS with datasets generated using functional genomics techniques [e.g., chromatin immunoprecipitation followed by sequencing (ChIP-seq), assay for transposase-accessible chromatin using sequencing (ATAC-seq), etc.] have found that for some complex traits, risk variants tend to reside and are enriched within regulatory sequence [9.Boer C.G. et al.Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.Cell. 2021; 184: 4784-4818.e17Abstract Full Text Full Text PDF PubMed Scopus (84) Google Scholar, 10.Wightman D.P. et al.A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease.Nat. Genet. 2021; 53: 1276-1282Crossref PubMed Scopus (199) Google Scholar, 11.Viñuela A. et al.Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D.Nat. Commun. 2020; 11: 4912Crossref PubMed Scopus (53) Google Scholar]. Functional studies have largely been enabled by recent developments in high-throughput methods that enable tissue-specific molecular profiling across several layers, such as on DNA methylation, chromatin accessibility, and transcript or protein level. Furthermore, large projects like GTEx [12.GTEx Consortium The GTEx Consortium atlas of genetic regulatory effects across human tissues.Science. 2020; 369: 1318-1330Crossref PubMed Google Scholar], ENCODE [13.Dunham I. et al.An integrated encyclopedia of DNA elements in the human genome.Nature. 2012; 489: 57-74Crossref PubMed Scopus (11513) Google Scholar], ROADMAP [14.Roadmap Epigenomics Consortium et al.Integrative analysis of 111 reference human epigenomes.Nature. 2015; 518: 317-330Crossref PubMed Scopus (3792) Google Scholar], and the Human Cell Atlas [15.Regev A. et al.The Human Cell Atlas.eLife. 2017; 6e27041Crossref Scopus (1102) Google Scholar] have made genome-wide, tissue-specific molecular maps publicly available, thus providing well-established resources of molecular landscapes (Box 1). These large, publicly available datasets enable investigation of disease-relevant tissues across several biological, multi-omics layers [16.Hasin Y. et al.Multi-omics approaches to disease.Genome Biol. 2017; 18: 83Crossref PubMed Scopus (1038) Google Scholar] and provide refined insights into the link between risk factors and disease.Box 1Public data resources for omics dataInternational collaborations have generated publicly available omics data resources which serve as reference data, for example, for functional follow-up studies of GWAS signals.Launched in 2010, the GTEx databasei provides a catalog of effects of genetic variants on gene expression and splicing of across 49 tissues, collected from postmortem samples of 838 individuals (version 8) [12.GTEx Consortium The GTEx Consortium atlas of genetic regulatory effects across human tissues.Science. 2020; 369: 1318-1330Crossref PubMed Google Scholar].ENCODEii was established in 2003 with a pilot project to describe functional elements in human and mouse genomes, initially focused on 1% of the genome [72.ENCODE Project Consortium Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project.Nature. 2007; 447: 799-816Crossref PubMed Scopus (4183) Google Scholar], but has expanded to the whole genome. The current version includes RNA transcription, DNA binding, chromatin modification and accessibility, DNA methylation, and replication timing data [73.ENCODE Project Consortium Expanded encyclopaedias of DNA elements in the human and mouse genomes.Nature. 2020; 583: 699-710Crossref PubMed Scopus (625) Google Scholar]. It describes 926 535 humans and 339 815 mouse candidate cis-regulatory elements.Roadmapiii presents human epigenomic data of 111 human tissues or cell types (further provides 16 cell types from ENCODE, thus 127 in total). It comprises histone modification patterns, DNA accessibility, DNA methylation, and RNA expression [14.Roadmap Epigenomics Consortium et al.Integrative analysis of 111 reference human epigenomes.Nature. 2015; 518: 317-330Crossref PubMed Scopus (3792) Google Scholar].The Human Cell Atlas (HCA) is an international collaboration that aims to generate reference maps of human tissues at single-cell resolution [15.Regev A. et al.The Human Cell Atlas.eLife. 2017; 6e27041Crossref Scopus (1102) Google Scholar]. For example, one recent HCA-associated study investigated 500 000 cells and provides a single-cell reference for 400 human cell types of 24 tissues or organs [74.Tabula Sapiens Consortium The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans.Science. 2022; 376eabl4896Google Scholar]. The HPC data coordination platformiv currently provides data from more than 26 million cells of 38 000 donors (7 July 2022).Furthermore, there are databases that provide disease-specific information. The Musculoskeletal Knowledge Portalv is a platform for genetic and genomic data relevant for musculoskeletal traits [75.Westendorf J.J. et al.The Musculoskeletal Knowledge Portal: improving access to multi-omics data.Nat. Rev. Rheumatol. 2022; 18: 1-2Crossref PubMed Scopus (3) Google Scholar]. It currently hosts 301 datasets for 281 traits.Similarly, there is a Type 2 Diabetes Knowledge Portalvi providing T2D-relevant data (349 datasets, 347 traits). Other T2D portals are Translational Human Pancreatic Islet Genotype Tissue-Expression Resource (TIGER), including omics and eQTL data from than 500 human islet samples [26.Alonso L. et al.TIGER: the gene expression regulatory variation landscape of human pancreatic islets.Cell Rep. 2021; 37109807Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar] and the Diabetes Epigenome Atlasvii [24.Chiou J. et al.Single-cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk.Nat. Genet. 2021; 53: 455-466Crossref PubMed Scopus (53) Google Scholar].For AD, the AD Knowledge Portal is an initiative that makes AD-relevant data accessible [76.Greenwood A.K. et al.The AD Knowledge Portal: a repository for multi-omic data on Alzheimer's disease and aging.Curr. Protoc. Hum. Genet. 2020; 108e105PubMed Google Scholar]. International collaborations have generated publicly available omics data resources which serve as reference data, for example, for functional follow-up studies of GWAS signals. Launched in 2010, the GTEx databasei provides a catalog of effects of genetic variants on gene expression and splicing of across 49 tissues, collected from postmortem samples of 838 individuals (version 8) [12.GTEx Consortium The GTEx Consortium atlas of genetic regulatory effects across human tissues.Science. 2020; 369: 1318-1330Crossref PubMed Google Scholar]. ENCODEii was established in 2003 with a pilot project to describe functional elements in human and mouse genomes, initially focused on 1% of the genome [72.ENCODE Project Consortium Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project.Nature. 2007; 447: 799-816Crossref PubMed Scopus (4183) Google Scholar], but has expanded to the whole genome. The current version includes RNA transcription, DNA binding, chromatin modification and accessibility, DNA methylation, and replication timing data [73.ENCODE Project Consortium Expanded encyclopaedias of DNA elements in the human and mouse genomes.Nature. 2020; 583: 699-710Crossref PubMed Scopus (625) Google Scholar]. It describes 926 535 humans and 339 815 mouse candidate cis-regulatory elements. Roadmapiii presents human epigenomic data of 111 human tissues or cell types (further provides 16 cell types from ENCODE, thus 127 in total). It comprises histone modification patterns, DNA accessibility, DNA methylation, and RNA expression [14.Roadmap Epigenomics Consortium et al.Integrative analysis of 111 reference human epigenomes.Nature. 2015; 518: 317-330Crossref PubMed Scopus (3792) Google Scholar]. The Human Cell Atlas (HCA) is an international collaboration that aims to generate reference maps of human tissues at single-cell resolution [15.Regev A. et al.The Human Cell Atlas.eLife. 2017; 6e27041Crossref Scopus (1102) Google Scholar]. For example, one recent HCA-associated study investigated 500 000 cells and provides a single-cell reference for 400 human cell types of 24 tissues or organs [74.Tabula Sapiens Consortium The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans.Science. 2022; 376eabl4896Google Scholar]. The HPC data coordination platformiv currently provides data from more than 26 million cells of 38 000 donors (7 July 2022). Furthermore, there are databases that provide disease-specific information. The Musculoskeletal Knowledge Portalv is a platform for genetic and genomic data relevant for musculoskeletal traits [75.Westendorf J.J. et al.The Musculoskeletal Knowledge Portal: improving access to multi-omics data.Nat. Rev. Rheumatol. 2022; 18: 1-2Crossref PubMed Scopus (3) Google Scholar]. It currently hosts 301 datasets for 281 traits. Similarly, there is a Type 2 Diabetes Knowledge Portalvi providing T2D-relevant data (349 datasets, 347 traits). Other T2D portals are Translational Human Pancreatic Islet Genotype Tissue-Expression Resource (TIGER), including omics and eQTL data from than 500 human islet samples [26.Alonso L. et al.TIGER: the gene expression regulatory variation landscape of human pancreatic islets.Cell Rep. 2021; 37109807Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar] and the Diabetes Epigenome Atlasvii [24.Chiou J. et al.Single-cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk.Nat. Genet. 2021; 53: 455-466Crossref PubMed Scopus (53) Google Scholar]. For AD, the AD Knowledge Portal is an initiative that makes AD-relevant data accessible [76.Greenwood A.K. et al.The AD Knowledge Portal: a repository for multi-omic data on Alzheimer's disease and aging.Curr. Protoc. Hum. Genet. 2020; 108e105PubMed Google Scholar]. In addition, (multi-)omics approaches have been applied to primary samples of several disease-relevant tissue types (Box 2), that is, tissue samples collected from patients or nondisease donors. Using primary disease-relevant tissue can provide novel insights into disease mechanisms that may not have been identified when using peripheral tissues or cellular models.Box 2Analyses to associate multi-omics data with diseasesA standard approach to link molecular data with a disease is to conduct differential analyses, for example, between cases and controls. This is similar to the case–control approach in GWAS. In contrast to genetic studies, in which signals are estimated to play causal role in disease (and not vice versa, as genotypes are not affected by diseases because they form at conception), the changes in molecular features (RNA or protein abundances, epigenomic marks, or chromatin states) could be consequences rather than driving risk factors of the disease. Thus, differential analyses identify markers that are not necessarily causally involved in the disease of interest.In addition, several approaches that integrate data across multiple omics layers have been developed [16.Hasin Y. et al.Multi-omics approaches to disease.Genome Biol. 2017; 18: 83Crossref PubMed Scopus (1038) Google Scholar]. A well-established example is the integration of genomic and gene expression data of matching samples to identify genetic variants that influence expression levels of a gene, termed expression quantitative trait loci (eQTLs), on a genome-wide scale [16.Hasin Y. et al.Multi-omics approaches to disease.Genome Biol. 2017; 18: 83Crossref PubMed Scopus (1038) Google Scholar]. The eQTL maps can be combined with GWAS results to identify molecular drivers (e.g., likely effector genes) through which risk variants exert their effects in disease-relevant tissue. In the context of complex diseases, these high-confidence effector genes may represent promising drug targets [77.King E.A. et al.Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval.PLoS Genet. 2019; 15e1008489Crossref PubMed Scopus (215) Google Scholar]. Other established multi-omics strategies infer information from networks [78.Ogris C. et al.Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data.Sci. Rep. 2021; 11: 6806Crossref PubMed Scopus (7) Google Scholar] or estimate low-dimensional representations from multi-omics datasets, for example, to stratify samples [79.Argelaguet R. et al.MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data.Genome Biol. 2020; 21: 111Crossref PubMed Scopus (170) Google Scholar]. A standard approach to link molecular data with a disease is to conduct differential analyses, for example, between cases and controls. This is similar to the case–control approach in GWAS. In contrast to genetic studies, in which signals are estimated to play causal role in disease (and not vice versa, as genotypes are not affected by diseases because they form at conception), the changes in molecular features (RNA or protein abundances, epigenomic marks, or chromatin states) could be consequences rather than driving risk factors of the disease. Thus, differential analyses identify markers that are not necessarily causally involved in the disease of interest. In addition, several approaches that integrate data across multiple omics layers have been developed [16.Hasin Y. et al.Multi-omics approaches to disease.Genome Biol. 2017; 18: 83Crossref PubMed Scopus (1038) Google Scholar]. A well-established example is the integration of genomic and gene expression data of matching samples to identify genetic variants that influence expression levels of a gene, termed expression quantitative trait loci (eQTLs), on a genome-wide scale [16.Hasin Y. et al.Multi-omics approaches to disease.Genome Biol. 2017; 18: 83Crossref PubMed Scopus (1038) Google Scholar]. The eQTL maps can be combined with GWAS results to identify molecular drivers (e.g., likely effector genes) through which risk variants exert their effects in disease-relevant tissue. In the context of complex diseases, these high-confidence effector genes may represent promising drug targets [77.King E.A. et al.Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval.PLoS Genet. 2019; 15e1008489Crossref PubMed Scopus (215) Google Scholar]. Other established multi-omics strategies infer information from networks [78.Ogris C. et al.Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data.Sci. Rep. 2021; 11: 6806Crossref PubMed Scopus (7) Google Scholar] or estimate low-dimensional representations from multi-omics datasets, for example, to stratify samples [79.Argelaguet R. et al.MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data.Genome Biol. 2020; 21: 111Crossref PubMed Scopus (170) Google Scholar]. This includes increased resolution into disease progression when affected primary tissues are investigated across disease stages. Here, disease-affected tissues may reflect unrelated disease processes which can be less relevant to prevention (but still relevant to treatment), whereas pre-diseased tissues may help elucidate the pathomechanism, which can be more relevant to prevention. In this review, we cover recent insights into four complex (Box 3) diseases provided by multi-omics studies on disease-relevant primary tissue (Figure 1 and Table 1). These affect different disease-relevant tissues and pose distinct challenges: (i) T2D is a clinically heterogeneous metabolic disease for which relevant tissue samples are difficult to access. (ii) Osteoarthritis is a joint disorder for which joint tissues are challenging to access and are not included in reference databases. (iii) AD is a neurodegenerative disease that affects the brain, a complex organ that can only be studied post-mortem. (iv) SLE is an autoimmune disease with large patient heterogeneity.Box 3The largest GWAS for type 2 diabetes, osteoarthritis, Alzheimer's disease, and systemic lupus erythematosusGWAS have revealed insights into the polygenic architecture of T2D, osteoarthritis, AD, and SLE.In T2D, the largest study to date comprised 1 339 889 individuals with 180 834 cases and 1 159 055 controls [80.Mahajan A. et al.Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation.Nat. Genet. 2022; 54: 560-572Crossref PubMed Scopus (73) Google Scholar]. Of these, the major part was of European descent (51.1%).For osteoarthritis, the largest GWAS investigated 826 690 individuals (177 517 cases and 649 173 controls), with more than 99.3% of European ancestry [9.Boer C.G. et al.Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.Cell. 2021; 184: 4784-4818.e17Abstract Full Text Full Text PDF PubMed Scopus (84) Google Scholar].The largest AD study investigated 1 126 563 individuals (90 338 cases, 1 036 225 controls) [10.Wightman D.P. et al.A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease.Nat. Genet. 2021; 53: 1276-1282Crossref PubMed Scopus (199) Google Scholar]. Another recent GWAS for Alzheimer's disease included fewer individuals in total (n = 788 989), but a higher number of cases (n = 111 326) [48.Bellenguez C. et al.New insights into the genetic etiology of Alzheimer's disease and related dementias.Nat. Genet. 2022; 54: 412-436Crossref PubMed Scopus (246) Google Scholar]. Both studies included individuals of European descent only.The largest SLE GWAS has been performed in 208 370 individuals (13 377 SLE cases, 194 993 controls), all of which are of East Asian descent [59.Yin X. et al.Meta-analysis of 208370 East Asians identifies 113 susceptibility loci for systemic lupus erythematosus.Ann. Rheum. Dis. 2021; 80: 632-640Crossref PubMed Scopus (54) Google Scholar].Table 1Overview of approach types and measured molecular omics levels in disease-relevant tissues in multi-omics studiesaAbbreviations: HiChIP, Hi-C chromatin immunoprecipitation; mux-seq, multiplexed single-cell RNA sequencing; scTHS-seq, sc-transposome hypersensitive sites sequencing; snDrop-seq, single-nucleus Droplet-based sequencing; WGBS, whole-genome bisulfite sequencing.DiseasePrimary tissueTypes of approachesOmicsType 2 diabetesPancreatic isletsBulk:450k, WGBSATAC-seqHi-CChIP-seqpcHi-CRNA-seqMass spectrometrySingle-cell/nucleus:snATAC-seqscRNA-seqDNA methylationChromatin accessibilityChromatin conformationProtein–DNA interactomePromoter capture chromatin conformationTranscriptomicsProteomicsSingle-nucleus (sn) chromatin accessibilitySingle-cell (sc) transcriptomicsOsteoarthritisCartilageBulk:450k, EPICATAC-seqRNA-seqMass spectrometryMethylationChromatin accessibilityTranscriptomicsProteomicsSynoviumBulk:RNA-seqMass spectrometryTranscriptomicsProteomicsAlzheimer's diseaseBrainBulk:ATAC-seqHiChIPRNA-seqChIP-seqMass spectrometrySingle-cell/nucleus:scATAC-seqscRNA-seqsnDrop-seqscTHS-seqChromatin accessibilityEnhancer connectomeTranscriptomicsProtein–DNA interactomeProteomics, phosphoproteomic, lipidomicssc chromatin accessibilitysc transcriptomicssn transcriptomicsn chromatin accessibilitySystemic lupus erythematosusBloodSingle cellmux-seqsc transcriptomicsBulk:RNA-seq, microarrayMass spectrometryNMR spectroscopyTranscriptomicsProteomics, metabolomicsMetabolomicsa Abbreviations: HiChIP, Hi-C chromatin immunoprecipitation; mux-seq, multiplexed single-cell RNA sequencing; scTHS-seq, sc-transposome hypersensitive sites sequencing; snDrop-seq, single-nucleus Droplet-based sequencing; WGBS, whole-genome bisulfite sequencing. Open table in a new tab GWAS have revealed insights into the polygenic architecture of T2D, osteoarthritis, AD, and SLE. In T2D, the largest study to date comprised 1 339 889 individuals with 180 834 cases and 1 159 055 controls [80.Mahajan A. et al.Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation.Nat. Genet. 2022; 54: 560-572Crossref PubMed Scopus (73) Google Scholar]. Of these, the major part was of European descent (51.1%). For osteoarthritis, the largest GWAS investigated 826 690 individuals (177 517 cases and 649 173 controls), with more than 99.3% of European ancestry [9.Boer C.G. et al.Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.Cell. 2021; 184: 4784-4818.e17Abstract Full Text Full Text PDF PubMed Scopus (84) Google Scholar]. The largest AD study investigated 1 126 563 individuals (90 338 cases, 1 036 225 controls) [10.Wightman D.P. et al.A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease.Nat. Genet. 2021; 53: 1276-1282Crossref PubMed Scopus (199) Google Scholar]. Another recent GWAS for Alzheimer's disease included fewer individuals in total (n = 788 989), but a higher number of cases (n = 111 326) [48.Bellenguez C. et al.New insights into the genetic etiology of Alzheimer's disease and related dementias.Nat. Genet. 2022; 54: 412-436Crossref PubMed Scopus (246) Google Scholar]. Both studies included individuals of European descent only. The largest SLE GWAS has been performed in 208 370 individuals (13 377 SLE cases, 194 993 controls), all of which are of East Asian descent [59.Yin X. et al.Meta-analysis of 208370 East Asians identifies 113 susceptibility loci for systemic lupus erythematosus.Ann. Rheum. Dis. 2021; 80: 632-640Crossref PubMed Scopus (54) Google Scholar]. T2D is a complex metabolic disease affecting more than 450 million people worldwide [2.Khan M.A.B. et al.Epidemiology of type 2 diabetes - global burden of disease and forecasted trends.J. Epidemiol. Glob. Health. 2020; 10: 107-111Crossref PubMed Google Scholar] and is characterized by impairment of insulin secretion and signaling, and of carbohydrate, lipid, and protein metabolism [17.Mahajan A. et al.Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.Nat. Genet. 2018; 50: 1505-1513Crossref PubMed Scopus (859) Google Scholar,18.Scott R.A. et al.An expanded genome-wide association study of type 2 diabetes in Europeans.Diabetes. 2017; 66: 2888-2902Crossref PubMed Scopus (440) Google Scholar]. The two key mechanisms in developing T2D include defective insulin secretion from the endocrine pancreatic beta cells and the lack/reduced response to insulin from insulin-sensitive tissues [19.Galicia-Garcia U. et al.Pathophysiology of type 2 diabetes mellitus.Int. J. Mol. Sci. 2020; 21E6275Crossref Scopus (567) Google Scholar]. Over 700 genetic risk loci have been implicated in T2D to date with >90% mapped to noncoding sequences [17.Mahajan A. et al.Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.Nat. Genet. 2018; 50: 1505-1513Crossref PubMed Scopus (859) Google Scholar,18.Scott R.A. et al.An expanded genome-wide association study of type 2 diabetes in Europeans.Diabetes. 2017; 66: 2888-2902Crossref PubMed Scopus (440) Google Scholar]. The majority of these variants (which explain 19% of T2D risk [20.Polfus L.M. et al.Genetic discovery and risk characterization in type 2 diabetes across diverse populations.HGG Adv. 2021; 2100029PubMed Google Scholar]) increase the risk of developing T2D mainly through effects on insulin secretion. To this end, studies of pancreas across multiple levels of expression are vital to gain insight into T2D molecular regulation. Despite pancreas being difficult to access, large efforts like GTEx have linked genomic variation to pancreatic gene expression. These resources constitute a valuable reference, but are not T2D specif